commit | a84a2cbe07dfe608acd79453affc53330ebab1f0 | [log] [tgz] |
---|---|---|
author | Tianqi Chen <tqchen@users.noreply.github.com> | Sat Apr 08 10:22:50 2023 -0400 |
committer | GitHub <noreply@github.com> | Sat Apr 08 07:22:50 2023 -0700 |
tree | 2c4650a33a66076688508f8f1e075e1c06ffa1aa | |
parent | 8e9216013ced6b9655359a696a5bb1bf68fdd638 [diff] |
[ARITH] Enhance CanProve to handle symbolic bound (#14523) This PR enhances CanProve to handle symbolic bound. Such analysis is essential to eliminate predicates in dynamic shape workloads. We also the int set analysis singlepoint check to avoid recursion and improve the overall analysis speed. Added CanProveSinglePoint to serve previous stronger checks. The new CanProve comes with additinal strength argument that can only be used in top-level setting with stronger analysis. Added comment for future implementation efficiency. Testcases are added to cover the cases.
Documentation | Contributors | Community | Release Notes
Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends.
TVM is licensed under the Apache-2.0 license.
Check out the TVM Documentation site for installation instructions, tutorials, examples, and more. The Getting Started with TVM tutorial is a great place to start.
TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community. Check out the Contributor Guide.
We learned a lot from the following projects when building TVM.